{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DuckDB\n",
    "\n",
    ">[DuckDB](https://duckdb.org/docs/api/python/overview) is a fast in-process analytical database. DuckDB is under an MIT license.\n",
    "\n",
    "In this notebook we are going to show how to use DuckDB as a Vector store to be used in LlamaIndex.\n",
    "\n",
    "Install DuckDB with:\n",
    "\n",
    "```sh\n",
    "pip install duckdb\n",
    "```\n",
    "\n",
    "Make sure to use the latest DuckDB version (>= 0.10.0).\n",
    "\n",
    "You can run DuckDB in different modes depending on persistence:\n",
    "- `in-memory` is the default mode, where the database is created in memory, you can force this to be use by setting `database_name = \":memory:\"` when initializing the vector store.\n",
    "- `persistence` is set by using a name for a database and setting a persistence directory `database_name = \"my_vector_store.duckdb\"` where the database is persisted in the default `persist_dir` or to the one you set it to.\n",
    "\n",
    "With the vector store created, you can:\n",
    "- `.add` \n",
    "- `.get` \n",
    "- `.update`\n",
    "- `.upsert`\n",
    "- `.delete`\n",
    "- `.peek`\n",
    "- `.query` to run a search. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic example\n",
    "\n",
    "In this basic example, we take the Paul Graham essay, split it into chunks, embed it using an open-source embedding model, load it into `DuckDBVectorStore`, and then query it.\n",
    "\n",
    "For the embedding model we will use OpenAI. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install llama-index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a DuckDB Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install duckdb\n",
    "!pip install llama-index-vector-stores-duckdb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index.core import VectorStoreIndex, SimpleDirectoryReader\n",
    "from llama_index.vector_stores.duckdb import DuckDBVectorStore\n",
    "from llama_index.core import StorageContext\n",
    "\n",
    "from IPython.display import Markdown, display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup OpenAI API\n",
    "import os\n",
    "import openai\n",
    "\n",
    "openai.api_key = os.environ[\"OPENAI_API_KEY\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download and prepare the sample dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2024-02-16 19:38:34--  https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 185.199.108.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 75042 (73K) [text/plain]\n",
      "Saving to: ‘data/paul_graham/paul_graham_essay.txt’\n",
      "\n",
      "data/paul_graham/pa 100%[===================>]  73.28K  --.-KB/s    in 0.06s   \n",
      "\n",
      "2024-02-16 19:38:34 (1.24 MB/s) - ‘data/paul_graham/paul_graham_essay.txt’ saved [75042/75042]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!mkdir -p 'data/paul_graham/'\n",
    "!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "documents = SimpleDirectoryReader(\"data/paul_graham/\").load_data()\n",
    "\n",
    "vector_store = DuckDBVectorStore()\n",
    "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
    "\n",
    "index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<b>The author mentions that before college, they worked on two main things outside of school: writing and programming. They wrote short stories and also tried writing programs on an IBM 1401 computer. They later got a microcomputer and started programming more extensively.</b>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "query_engine = index.as_query_engine()\n",
    "response = query_engine.query(\"What did the author do growing up?\")\n",
    "display(Markdown(f\"<b>{response}</b>\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Persisting to disk example\n",
    "\n",
    "Extending the previous example, if you want to save to disk, simply initialize the DuckDBVectorStore by specifying a database name and persist directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save to disk\n",
    "documents = SimpleDirectoryReader(\"data/paul_graham/\").load_data()\n",
    "\n",
    "vector_store = DuckDBVectorStore(\"pg.duckdb\", persist_dir=\"./persist/\")\n",
    "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
    "\n",
    "index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load from disk\n",
    "vector_store = DuckDBVectorStore.from_local(\"./persist/pg.duckdb\")\n",
    "index = VectorStoreIndex.from_vector_store(vector_store)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<b>The author mentions that before college, they worked on two main things outside of school: writing and programming. They wrote short stories and also tried writing programs on an IBM 1401 computer. They later got a microcomputer and started programming more extensively.</b>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Query Data\n",
    "query_engine = index.as_query_engine()\n",
    "response = query_engine.query(\"What did the author do growing up?\")\n",
    "display(Markdown(f\"<b>{response}</b>\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metadata filter example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is possible to narrow down the search space by filter with metadata. Below is an example to show that in practice. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index.core.schema import TextNode\n",
    "\n",
    "nodes = [\n",
    "    TextNode(\n",
    "        **{\n",
    "            \"text\": \"The Shawshank Redemption\",\n",
    "            \"metadata\": {\n",
    "                \"author\": \"Stephen King\",\n",
    "                \"theme\": \"Friendship\",\n",
    "                \"year\": 1994,\n",
    "                \"ref_doc_id\": \"doc_1\",\n",
    "            },\n",
    "        }\n",
    "    ),\n",
    "    TextNode(\n",
    "        **{\n",
    "            \"text\": \"The Godfather\",\n",
    "            \"metadata\": {\n",
    "                \"director\": \"Francis Ford Coppola\",\n",
    "                \"theme\": \"Mafia\",\n",
    "                \"year\": 1972,\n",
    "                \"ref_doc_id\": \"doc_1\",\n",
    "            },\n",
    "        }\n",
    "    ),\n",
    "    TextNode(\n",
    "        **{\n",
    "            \"text\": \"Inception\",\n",
    "            \"metadata\": {\n",
    "                \"director\": \"Christopher Nolan\",\n",
    "                \"theme\": \"Sci-fi\",\n",
    "                \"year\": 2010,\n",
    "                \"ref_doc_id\": \"doc_2\",\n",
    "            },\n",
    "        }\n",
    "    ),\n",
    "]\n",
    "\n",
    "vector_store = DuckDBVectorStore()\n",
    "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
    "index = VectorStoreIndex(nodes, storage_context=storage_context)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the metadata filters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters\n",
    "\n",
    "filters = MetadataFilters(filters=[ExactMatchFilter(key=\"theme\", value=\"Mafia\")])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the index as a retriever to use the metadatafilter option. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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'{\"id_\": \"736a1279-4ebd-496e-87b5-925197646477\", \"embedding\": null, \"metadata\": {\"director\": \"Francis Ford Coppola\", \"theme\": \"Mafia\", \"year\": 1972, \"ref_doc_id\": \"doc_1\"}, \"excluded_embed_metadata_keys\": [], \"excluded_llm_metadata_keys\": [], \"relationships\": {}, \"text\": \"\", \"start_char_idx\": null, \"end_char_idx\": null, \"text_template\": \"{metadata_str}\\\\n\\\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\\\n\", \"class_name\": \"TextNode\"}', '_node_type': 'TextNode', 'document_id': 'None', 'doc_id': 'None'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='The Godfather', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=0.7543986421543848)]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retriever = index.as_retriever(filters=filters)\n",
    "retriever.retrieve(\"What is inception about?\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "llama-index-dev",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
